Patient activity signature analysis

• US 9,368,014 B1
• Filed: 10/20/2014
• Issued: 06/14/2016
• Est. Priority Date: 10/18/2013
• Status: Active Grant

• Current Assignee: Barry B. Bittman
• Original Assignee: Barry B. Bittman
• Inventors: Bittman, Barry B.
• Primary Examiner(s): Pham, Toan N
• Application Number:
  US14/518,186
• Time in Patent Office: 603 Days
• Field of Search: 340/539.12, 340/573.1, 340/522, 600/301, 600/595
• US Class Current: 1/1
• CPC Class Codes: G08B 21/0453 worn on the body to detect health condition by physiological monitoring, e.g. electrocardiogram, temperature, breathing detecting, measuring or recording for diagnostic purposes A61B5/00
• CPC Class Codes: G08B 25/08 using communication transmission lines G08B13/19658, G08B21/0286, G08B25/016 take precedence
• CPC Class Codes: G16H 40/63 for local operation
• CPC Class Codes: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Tools, strategies, and techniques are provided for remote, early detection of patient health condition based on deviations from established patterns of patient movement or other activity. Once established, a patient activity signature can be compared to observed patient behavior to detect unacceptable or unexpected deviations from the activity signature. Deviation from a patient movement signature can be detected and can cause an alert or other notification to be generated or communicated to a physician, for example, or other appropriate health care providers.

Claims

1. A method for analyzing a condition of a patient:

generating, by an electronic computer processor, at least one progressively established patient activity signature in connection with previously observed activity or movement of a patient derived at least in part from at least one electronic sensor;

collecting, by the processor, current movement data associated with the patient;

comparing, by the processor, the current patient movement data against the at least one progressively established patient activity signature; and

,

generating, by the processor, at least one notification in association with comparing the current patient movement data to the at least one progressively established patient activity signature.

2. The method of claim 1, further comprising detecting, by the processor, at least one deviation from the at least one progressively established patient activity signature.

3. The method of claim 2, further comprising communicating an alert to at least one health care provider in response to the detected deviation in the at least one progressively established patient activity signature.

4. The method of claim 3, further comprising communicating the alert with at least one dashboard screen.

5. The method of claim 1, further comprising receiving, by the processor, data representative of movement or activity of the patient from an activity detection device.

6. The method of claim 5, wherein the activity detection device comprises at least one of a motion sensor, a tilt sensor, an inertia sensor, a global positioning system, an accelerometer, or a reasonable combination thereof.

7. The method of claim 5, wherein the activity detection device comprises a wireless-enabled device.

8. The method of claim 7, wherein the device comprises a wireless transmitter structured for sending data to a relay station.

9. The method of claim 1, further comprising receiving, by the processor, data associated with the at least one progressively established patient activity in a data analytics module.

10. The method of claim 9, further comprising operatively associating the data analytics module with a machine learning module programmed for determining at least one pattern associated with behavior of the patient.

11. The method of claim 1, further comprising quantifying motion of the patient in terms of at least one of magnitude, duration, frequency, temporal occurrence, or a combination thereof.

12. The method of claim 11, further comprising plotting the quantified motion over time to create a graphical representation of a spectrum of activity for the patient.

13. A system for analyzing a condition of a patient:

an electronic computer processor programmed for executing instructions associated with at least one module;

a data analytics module programmed for generating at least one progressively established patient activity signature in connection with previously observed activity or movement of a patient derived at least in part from at least one electronic sensor;

a patient activity monitoring module programmed for collecting current movement data associated with the patient;

wherein the data analytics module is programmed for comparing the current patient movement data against the at least one progressively established patient activity signature; and

,

an alert notification module programmed for generating at least one notification in association with comparing the current patient movement data to the at least one progressively established patient activity signature.

14. The system of claim 13, further comprising the data analytics module programmed for detecting at least one deviation from the at least one progressively established patient activity signature.

15. The system of claim 13, further comprising the alert notification module programmed for communicating an alert to at least one health care provider in response to the detected deviation in the at least one progressively established.

16. The system of claim 13, further comprising the patient activity monitoring module programmed for receiving data representative of movement or activity of the patient from an activity detection device.

17. The system of claim 16, wherein the activity detection device comprises at least one of a motion sensor, a tilt sensor, an inertia sensor, a global positioning system, an accelerometer, or a reasonable combination thereof.

18. The system of claim 16, wherein the activity detection device comprises a wireless-enabled device.

19. The system of claim 18, wherein the device comprises a wireless transmitter structured for sending data to a relay station.

20. The system of claim 13, further comprising a machine learning module programmed for determining at least one pattern associated with behavior of the patient.

21. The system of claim 13, further comprising the data analytics module programmed to quantify motion of the patient in terms of at least one of magnitude, duration, frequency, temporal occurrence, or a combination thereof.

22. The system of claim 21, further comprising a module programmed for plotting the quantified motion over time to create a graphical representation of a spectrum of activity for the patient.